{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index=pd.read_csv(\"economic_index.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>index_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>2.75</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  year  month  interest_rate  unemployment_rate  index_price\n",
       "0           0  2017     12           2.75                5.3         1464\n",
       "1           1  2017     11           2.50                5.3         1394\n",
       "2           2  2017     10           2.50                5.3         1357\n",
       "3           3  2017      9           2.50                5.3         1293\n",
       "4           4  2017      8           2.50                5.4         1256"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unneccessary columns\n",
    "df_index.drop(columns=[\"Unnamed: 0\",\"year\",\"month\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>index_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.75</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   interest_rate  unemployment_rate  index_price\n",
       "0           2.75                5.3         1464\n",
       "1           2.50                5.3         1394\n",
       "2           2.50                5.3         1357\n",
       "3           2.50                5.3         1293\n",
       "4           2.50                5.4         1256"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "interest_rate        0\n",
       "unemployment_rate    0\n",
       "index_price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check null values\n",
    "df_index.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x276860727c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets do some viualization\n",
    "import seaborn as sns\n",
    "sns.pairplot(df_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>index_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>interest_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.925814</td>\n",
       "      <td>0.935793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployment_rate</th>\n",
       "      <td>-0.925814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.922338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index_price</th>\n",
       "      <td>0.935793</td>\n",
       "      <td>-0.922338</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   interest_rate  unemployment_rate  index_price\n",
       "interest_rate           1.000000          -0.925814     0.935793\n",
       "unemployment_rate      -0.925814           1.000000    -0.922338\n",
       "index_price             0.935793          -0.922338     1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'unemployment rate')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualiza the datapoints more closely\n",
    "plt.scatter(df_index['interest_rate'],df_index['unemployment_rate'],color='r')\n",
    "plt.xlabel(\"Interest rate\")\n",
    "plt.ylabel(\"unemployment rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "##independent and dependent features\n",
    "X=df_index.iloc[:,:-1]\n",
    "y=df_index.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.75</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   interest_rate  unemployment_rate\n",
       "0           2.75                5.3\n",
       "1           2.50                5.3\n",
       "2           2.50                5.3\n",
       "3           2.50                5.3\n",
       "4           2.50                5.4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1464\n",
       "1     1394\n",
       "2     1357\n",
       "3     1293\n",
       "4     1256\n",
       "5     1254\n",
       "6     1234\n",
       "7     1195\n",
       "8     1159\n",
       "9     1167\n",
       "10    1130\n",
       "11    1075\n",
       "12    1047\n",
       "13     965\n",
       "14     943\n",
       "15     958\n",
       "16     971\n",
       "17     949\n",
       "18     884\n",
       "19     866\n",
       "20     876\n",
       "21     822\n",
       "22     704\n",
       "23     719\n",
       "Name: index_price, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='interest_rate', ylabel='index_price'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df_index['interest_rate'],df_index['index_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='interest_rate', ylabel='unemployment_rate'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df_index['interest_rate'],df_index['unemployment_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='index_price', ylabel='unemployment_rate'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fqgp6rVS2MVNQsZJBGfBV4L3pQ48D/6yq0aJEmYOpngwyhWNJusIxwrHCV/Ac6AqzsbmVx3cdIpHOCtVBLx+7sJ6rzl9IeZGWjUJ6wjzooypY3E5yxpjSKVYy+JiqPjDWsfEwnZLBgGhioA5S/pPNA46ku6/9MqP7WoXfw9UrF/HRMbqv5crrdlFX7itqojHGlEaxksE2VV051rHxMB2TwYBEMkV3kfortPdF+ckw3deuOn8hHyty97WA182siuznE4wx46+gZCAilwNXAB8H7s+4qQo4S1XXFCvQbE3nZDCgmJPN3aE4D25r4+EX9g92X/N7XFx53gKuXdVQ1O5rhe5PMMaUTqHJ4DzgfOAbwNcybuoFfqeqnUWKM2szIRkMKOZkc28kzsMvON3XeiMZ3ddWLOC6NcXrvuYSobbM5hOMmWyKNUzkVdV4USPL00xKBplCMScpFDrZHIqlu69taaMr3X3N7RLed9Y8rl/byKIidV+zUtnGTC7FSganAt8EzgIG30Kq6qi7hkSkBrgDWAEocLOqPp9xuwDfxRmKCgGfUtVtoz3mTE0GA4o12RxJd1+7f0j3tXedMZcb1zayeFZ5UeKt8HuYVeG3oSNjJlixksFzwD8C/wZcCXw6ff4/jnHej4FnVfUOEfEBZaralXH7FcAXcJLBWuC7qrp2tMec6clgQLEmm53ua4fY2NxyQve1S9Ld15YXofuaS5wua9XBydllbbxLoBszEYqVDLaq6oUi8pKqnpM+9qyq/sko51QBO4BlOsITicgPgKdVdWP6+93Apap6cKTHtWRwomRK6Y3E6Q4X1oktke6+dm9zK/u7jhexe9vyWaxf18gZ86sKjtXvdTOr3Depho4msgS6MeOpWCWsIyLiAl4Xkb8QkY8AY/2lLAOOAneJyAsicoeIDB17WAS0Znzflj5msjRQMbWxroxZFf68y2h73C4uP2cBP/r0ar5yxRksrnOKyP3xzXb+7J4X+NJPX2Tn/u6CYo3GkxzoCnOsL0qqSC1ECzWRJdCNmSxyedX4a6AMpwzFhcB64JNjnOMBVgLfT9cz6scpZ5FpuIHkk14lROQWEdkiIluOHj2aQ9gzx0DXsoa6ILMr/Xm3s3S7hHefOY87P7WKf7zyLJbPcfL35rc6+cv7tvPFn2xnW0tnQfMVPeE4rZ0heiMTvyahtTN0UskOK69tZpqsto2KiBv4uKr+HdCHM1+QjTagTVU3pb9/kJOTQRuQWT6zHjgw9IFU9XbgdnCGibJ8/hlJRKgKeKkKeOmPJujKs7eCS4R3nDaHS06dzfN72rm7qYXdh3rZ3trN9tYXOWuB031t7dKTu69lI5lSjvZG6YkkmD2BG9YmUwl0YyZKVm8dVTUJXCg5/sWr6iGgVUROTx+6DHh5yN0eAT4hjnVA92jzBSY35X4Pi2qCLKwJ5l0yQkR42/LZfO8Gp/vaOYucuYOXD/bwlYd3cuuGbTw7pPtaLqLphjrH+qIFzXnka7KWQDdmPOUygfx/A6cCD+AM9wCgqg+Ncd75OEtLfcAenKuKa9Pn3pZOMP8JvB9naemnVXXU2WGbQM5fIpmiJ5KgN1LYZPOO1i42NO1ja0vX4LGls8tZv7aRS06bk/cyUrfLWXVUVcT6SdmYCiXQjSlUsVYT3TXMYVXVmwsJLh+WDAqnqvRGE3SH4sST+e9sfvlADxs27aNpT8fgsYbaIDeubeSyM+flnRR8Hhezyv0EfZNn1ZExU11RkkEWT/L3qvrNojzYGCwZFFcolqAzVFjP5tcP97JhUwvPvn5s8NiC6gDXr2nkvWfNw+fJbzI76HNTWza5lqIaM1WNVzIYtwqmlgxKIxxL0hmKESkgKew91s+Gpn38/rWjg4125lT4uXZ1Ax84Zz7+PF/UK/weast9ea+QMsaMXzIYbIdZapYMSisST9IViufc9zhTa0eIe4fpvvbxVfVcmWf3NWeVlIca67JmTF7sysDkJZpI0h2K0x/LvwbSwe4w9zW38tjOE7uvXXPhIj58/qK8Vji5RKgp81Id9FpVVGNyYFcGpiDFWIF0tDfK/ZtbefSlg4OluCv8Hq6+YBFXr1xEVR41izwuF7Xl3qJ2bjNmOivWaqKLVfUPIx0Tka+o6v8uONosWDKYGAMrkHoK6K3Q0R/jgS2t/HzHASJx5zGC3nT3tVX11ObRfc3ncVpvZm4aM8aczNpemqILx5L0ROL0R/ObV+gOx/nptjYe3raf/ozuax88dwEfX9XAnMrcu68FvG7qJlkRPGMmk9GSwZhvpUTkIuBtwBwR+WLGTVWA/dVNYqUsyxz0uQn63MSTKXryKKO9+2AvO9t6KPO7qQx46QnHCcWT/HTbfh7ZcYDL093X5ufQfS2SLoJX7vdQW+bLezlrtqzstZlOsvlr8QEVOImjMuOjB7imdKGZQgyUZT7SG6Em6OVIb4SvPbKLp189UtTn8bpdzKrw01hXlnXv4+Y9HXz3qddp749SE/TidQuVQQ+Xnz2f2jIv8aTyyI4D3HRnM//6+G72d4bHfMxM/dEE+9OVURMFbKgbzXj9fI0ZL7kMEy1W1X0ljicrNkw0tutvbzqp+FoolmBuZYCNt6wr2fNms7P5i/fvoL0/esLy0nA8yaxyP//76hX86qWD3Le5lWN9hXdfG6jkWh30FnU56kT9fI0pREHDRBn8InI7sCTzPFV9V2HhmVJo7QxRM2SFzniUZR5aMbU7HD9pE9vBnjBVgRP/6wW8Lg71hAl43Vy9sp4PnruQx3cd4t7mFg73RPnNK0f47StH+JPTZnPT2sUsn5td9zVVpSsUoyccL+py1In6+RpTKrkkgweA23CKzhXWkd2U3GQoy1zu91Du9xCJJ+kJH9+vsKAqeNKVQSSeYn5VcPB7n8fFlect5PIV83nylSNsbG6hrTPMM68d45nXjnHRMqf72pkLsuu+llKloz9GTzhRlOWok+Hna0wx5TLDllDV76tqs6puHfgoWWSmIJOpLHPA62ZuVYCG2iDVQS/Xr2kgkVLC8SSK8zmRUq5b3XDSuR63i8tXzOeuT63mq1ecweJZzovt83va+fN7X+B/PvgiL7Vl330tkUpxtDdKW2eooB3Wk+nna0wx5DJn8HXgCPAwEB04rqodI51TKjZnkJ3JWpY5lVIee+kgdzy3l4PdYeZXBbludQNrltWNfa4qz71+jA1NLbxxtG/w+Hn11dy0bjEXNNbkNAxUSCG8yfrzNWYkxdpnsHeYw6qq4/5WyJLB9KCq9KXnFXLdxKaqbNrbwd1N+3jlYO/g8bMWVLJ+3eKcu6+V+z3UlHknrNuaMeNhXMpRjCdLBtNPOJakKxwjHMttOkpV2bqvk7ubWnhp//HholPnVrB+3WIuPmUWrhySQoXfKYRX6j0KxkyEYl0ZlAFfBBpV9RYRORU4XVUfLV6o2bFkMH3FEim6w3H6oomci+PtaO1iw6YWtu7rHDy2dHY5N65t5B05dF8TESoDzsY1q45qppNiJYP7ga3AJ1R1hYgEgedV9fyiRZolSwbTXzKl9Ebi9IQTJFK5DSEN132tfqD72hlz8WTZE8Gqo5rppljJYIuqrsqsTioiO1T1vCLGmhVLBjNHIfMKI3dfa+C9Z83PeijI43JRXealKuCxpGCmtGIlgz8ClwF/UNWVIrIc2Kiqa4oXanYsGcxMoViCrtDJm9jGsvdYP/dsauHp3UcK6r7mdbuoKbOS2WbqKlYyeA/wD8BZwBPAxcCnVPXpIsWZNUsGM1u+ndiG675WW+bl46sa+NB5Cwn6sk8KdeW+vBrzGDORiraaSERmAesAAZpU9dgYpyAibwG9OLuWE0MDEZFqYAPQiLMj+juqetdoj2nJYPIaz0qesUSKrnCM/mhundgOdUfY2NzCr3cdIp50zqsKeLjmwno+fMEiKrJ8kfd73dSWea2PgpkyipkMzuXk2kQPjXHOW8CqkRKHiHwFqFbVL4nIHGA3MF9VYyM9piWDyWmgkqfXLQS9bsLxJPGk8o0PnV3SzViJpLMCKdcy2kd7o9y/pZVHXzzefa3c7053X6unOsvua36vm5qg164UzKRXlEJ1IvJfwLnALmBgJk+BUZNBFhSoFGdmrgLoAPKvE2AmzA+e2YPXLYPvlMt8HkKxBD94Zk9Jk4EnXUa7tsxHb8SZbM5mBdKcSj9/8c5TuGFNIw9ubeNn2/fTH01yd1MLD27dz1XnL+SaC+upKx+9+1o0nuRwPInf66auzJf1cJMxk0kucwYvq+pZOT+Bs3O5E+dF/weqevuQ2yuBR4AzcPokXKuqvxzmcW4BbgFobGy8cN++SVFN22R4+7efombIMkxVpTsc59kvjV9x23xXIHWH4zy0rY2HXnCSAjjd1z5w7gKuzaH7mnVcM5PVaFcGuWyzfF5Eck4GwMXp1piXA38uIpcMuf19wHZgIXA+8J8iclIpSlW9XVVXqeqqOXPm5BGGKbWG2jLCQ1b6TEQlT2fTmJf62jIWVAezHtOvDnr59MVL2fjZddx88RKqAh6iiRQPbdvP+js38W+/eY1DPZExH2eg49rB7nDOK5+MmSi5JIMf4ySE3SLyooi8JCIvjnWSqh5Ifx4ocjd0KeqngYfU8QawF+cqwUwxk7GSZ9DnZn51gEW1QSqy3CdQEfCwft1iNv7pOj53ybLB7mu/2HGQm+5s5v/8Orvua+GYkxQOdUcsKZhJL5dhojdwylG8xPE5A0brfiYi5YBLVXvTXz8JfENVf51xn+8Dh1X16yIyD9gGnDfaSiWbQJ68Jnslz3wmm6PxJL986RD3bW45qfvaDWsbWZJl9zUrhmcmWrH2GTyVa1czEVmGczUAzmT1var6LyJyK4Cq3iYiC4EfAQtwlqx+S1U3jPa4lgxMoVIppSfHchexRIonXj7EvZtaB4eLBPiTU2ezft1iTsmy+1qZz0kKNqdgxluxksH3gBrgF5zYz6DQ1UQ5s2RgiiWbns1DJZIpfvPKEe5Nd18bsG5ZHTetW5x197WA102N7VMw46hYyWC4jWCqqjcXElw+LBmYUgjFnBVI2ZbRTqaUp3cf5Z5N+3ir/Xjv4wsX13LTukbOra/J6nECXqfBji1JNaVWrGRQNxFdzYZjycCUUjThlLvoj2a33SWlynNvpLuvHTnefe3cdPe1lVl2Xyuk65ox2ShWMngdZwnoXcBjOoFdcSwZmPEQS6ToCsXoyzIpjNR97cwFldyUQ/c1SwqmVIqVDAR4N3AzzvLQ+4EfqeprxQo0W5YMzHiKJ1N0hbJvuKOqbGvpYkPTPna0He++dsrcCtava+Ttp8zOqvta0OemJmjDR6Z4it72UkTeiVNcrhzYAXxZVZ8vKMocWDIwEyGfZakvtnWxoamFLRnd15bMKuPGtYu59PTsuq/ZRLMplmJdGcwC1gM3AYeBO3HKSJwPPKCqS4sSbRYsGZiJlEopvZEEPZHsVyC9crCHDU0tPL+nffBYfW2QG9Y08u4zs+u+5nG5qAx4qAx4su7WZkymYiWD14C7gbtUtW3IbV9S1W8XHGmWLBmYyaI/mqArHCea5Q7jN470sWHTPp597RgDf3nzq5zua+87+8Tua817OrhvcysHe8IsqApy3eoG1iyrA5xd0nVlPksKJidFmzNQVU0XllNV7RvzpBKxZGAmm0g8SWcolvWy1Lfa+7mnqYXfZXRfm13h47rVDVxxzgJebO3mu0+9jsclBLwuIvEUiZTyV+86dTAhODWYPNSW+bIabjKmWMlgBc6VQR3OxsujwCdVdWexAs2WJQMzWYVjTlLIthZRW2eIjc2tPPHyYZLprFBb5iXodSMC5RnzBOF4klnlfv6fa09sO+4SoSropSboxWVJwYyiWFVLbwe+qKqLVbUR+Nv0MWNMWtDnZmFNkAXVwaxWAdXXlvF37zuduz+zhg+dtxCvW+gMxTnQHeFgd4T2/thgkgh4XRzqOblAXkqVrlCM1s4Q3aE4qdSErfo2U1guyaBcVX838E2693F2FbqMmWGCPjcLqoMsrAlm1QFtflWAv373qWz4zFo+unIRIpBSaO+Psbe9n2N9UfqjSeZXBUd8jGRKae+P0toZoisUs6RgcpJLMtgjIv9LRJakP/4Bp9y0MWYEAa+beVXpEtpZJIU5lX7+/J2n8JX3n0G5343gJIWOUJyD3RGqgh46+kfsCAs4SaGj37lS6My4sjBmNLnMGdQC/wS8HWfO4Bng66raOeqJJWBzBmaqymVXc/OeDjY07WNvez/heHJwotnncfHBcxZw7ersuq+JCBV+D9VB7wmrlczMU/RNZxPNkoGZ6mKJFF3hGH2R7Epd9EUT/Hz7fh7Y0kZP+hyvW3j/2fO5bk0DC6pHHj7KVOH3UG09FWasgpKBiPwCGPFOqvqhwsLLnSUDM13kWuoiHEvyyI4D/GRLK52hOOA02nnPWfO4YU0jDXXZtRi1ngozU6HJ4B2j3a6qvy8gtrxYMjDTTa6lLqLxJL/aeYj7mls52ue0F3EJXHr6XG5c28jS2dmt7bD6RzNL0YaJRMSH059Ygd2qOvpMVolYMjDTVTKl9ITj9ETiWU38Ot3XDrOxuYWD3ZHB428/ZTbr1zVy2rzKrJ7X73VTHfRmNcltpq5ibTr7AHAb8CbOBPJS4HOq+lixAs2WJQMz3Q205ewOZ5cUEskUT716hHs2tdCa0X1t7VKn+9pZC7PrvuZ1u6gKeqkKeLIqt22mlmIlg1eBD6rqG+nvlwO/VNUzihZpliwZmJlCVemJOG05s+nVnEwpz7x2lA2bWth7rH/w+IWNNay/aDHnZdl9ze0SqgJeqoJeK3UxjYyWDHK5JjwykAjS9gBHCorMGDMqEaE6/U49m6TgdgnvPGMu7zh9Dn94o50NTft4/UgfW1u62NrSxTmLqrlpXSMXLq4d9Z1/MqV0hmJ0h+NUBJxlqV4rijet5XJl8H1gMfATnDmDjwG7gT8AqOpDJYrxJHZlYGYqVaUnnKArnN1mMlWl+a0O7n6+hZcP9gweP2N+JevXNXLRsllZDwdV+D1UBW0F0lRWrGGiu0a5WVX15hHOewvoBZJAYrhARORS4N8BL3BMVUddwWTJwMx02cwpZJbAnl8ZYPXSOrbs62B76/Hua8vnlLN+3WL+5NTsuq+Bs6u6OujNqszGcJ5+9Qg/eGYPrZ0hGmrL+Nwly7j0jLl5PZbJzYRuOksng1WqemyE22uAPwLvV9UWEZmrqqMOP1kyMMYxUlJo3tMxYgnsoM/Nhk372PzW8eIBi2eVsX5tI5eePjfrOQKv20V1mZdKf/aTzU+/eoSvPbILr1sIet2E40niSeUbHzrbEsI4KNaVwVLgC8ASMuYaxtp0lkUy+DNgoar+Q1aBYMnAmKEGho+6w86cwhfv30F7f5RgxpDO0BLYrxzs4Z5NLfzxzePd1xbVBLlhbSPvybL7GjjzFJUBL9VZTDZff3sTR3ojJ7TwDMUSzK0MsPGWdbn8k00eijWB/DOcVpe/ALLr9edQ4AkRUeAHqjq07PVpgFdEngYqge+q6n8PfRARuQW4BaCxsTGHpzdm+hMRqsu8VAU99EYTHOoJUxk48c97aAnsMxdU8X99eMUJ3df2d4X518d389/Pv8X1axp5/5Dua8NJppwS2t3hOOV+ZwhppHIXrZ0haoLeE44FvW7aOkN5/stNseSSDCKq+h95PMfFqnpAROYCT4rIq6r6zJAYLgQuA4LA8yLSpKqvZT5IOoncDs6VQR5xGDPtiThLQpfMKudQTwS/xzVY5iISTw1bAvuUuRV8/cqzeau9n3s3tfDUq0c43BPl33/zOnc37eO61Q184JwFY04cqyp9kQR9kQRBn5MUMq8AABpqy066MgjHk9TXZldGw5ROLmvFvisi/ygiF4nIyoGPsU5S1QPpz0eAh4E1Q+7SBvxaVfvTQ0nPAOdhjMnbre9YTkohkUrhcQnRRJJESrludcOI5yyZVc5XrjiTH396DVesmI/bJbT3xfj/fvcmN96xifuaWwjFsiusF44lOdQdobUjRE8kPpiQPnfJMuJJJRRzajGFYgniSeVzlywryr/b5C+XOYNvAjfh7EAeGCZSVX3XKOeUAy5V7U1//STwDVX9dcZ9zgT+E3gf4AOagetGa6dpcwbGjG1g1U5bZ4j62jJuvngJ5zbU0J9F+WyAQz0R7m9u5Vc7DxJPOq8TVQEPH11Zz0cuWERFIPuBhYF5haqAh+deP3ZCXLaaaPwUcwfyubnUIxKRZThXA+AMB92rqv8iIrcCqOpt6fv9HfBpnCRzh6r++2iPa8nAmPxFE0m6QvGsk8Kxvig/2dLKL3YcJJpw3geW+9x8+IJFXLOynuoy7xiPcJyIUO5z236FCVKsZHA/8IWxln2OB0sGxhQu16TQGYrxwJY2fr79AOF4EoCAx8WV5y3k2tUN1JX7cnp+n8epg5TL0lRTmGIlg6eBc4HNQHTguPUzMGZqyzUp9ITjPPTCfh7atn+wY5vP4+ID5yzg2lX1zK0K5PT8HpeL6qCXyoAHl9VBKqliJYNhdwVbPwNjpodoIkl3utFONvqiCR7ZfoAHtrbRHXYa7XhcwvtXzOe61Q0srMmu+9oAK45XesXsZ7AYOFVVfyMiZYBbVXuLFGfWLBkYUzq59GkGZ2noL3Yc4Cdb2ujod6YUXQLvPnMeN6xtpDHL7msDRIRyv9N0x3o2F1exrgz+FGfTV52qLheRU4HbVPWy4oWaHUsGxpRerkkhGk/y2M5D3Le5lSO9zkiyAJeePof16xZn3X0t00j7FUx+ipUMtuPsEdikqhekj72kqucUK9BsWTIwZvxEE0k6++NZ7zGIJ1M8sesw9xbYfS1TPnWQzMmKVY4iqqqxgV+EiHhwSk0YY6Yxv8fN/Go3kXiSjv4YkfRKopF43S4+cO4C3r9iPr999Qj3NO2jtTPMc28c47k3jrF2aR3r1zVy9sLqrGOIJ1Mc643S2R+zeYUSySUZ/F5EvgIEReQ9wJ/h1CkyxkwShZSHHuvcgNfNwpog4ViSztDISSGzdPaCqiCff8dyIokkG5pa2HOsn017O9i0t4OVjTWsX7eY8+qrs363P9B0pyscT/dX8IxYB8nkJpdhIhfwGeC9OEOBj+NsEBv3qwMbJjLmZIWUh87n3FAsQUd/jFjieN3K0Upnr1payx/faGfDpn28drhv8JwVC6u46aLFrBqj+9pIbF4hexPaz6AULBkYc7JCykMXcm5fNEFnf4x4MrvS2arK5rc6ubtpH7sOHO++dvr8StavbeRty7PvvpbJ63Y2sVUFbF5hJEWZMxCRi4Gv47S+9OBcHaiqWoUpYyaBQspDF3Juhd9Duc+ddelsEWHN0jpWL6nlhdYuNjS1sL21i92HevlfP9/FsjnlrF+7mEtOy777GjjzCu19UbpCscE6SNn2ZDC5zRncCfwNsBWnhaUxZhIppDx0oaWlh5bO9nlkcHnJSKWzRYSVjbWsbKxl5/5uNjTto/mtTvYc7ecbj77M4roybljbyLvOyL77GuTWX8Ecl0va7FbVx1T1iKq2D3yULDJjTE4KKQ9drNLSg6WzkylcLohkUTobYMWiar710XP5/o0rufiUWQDs6wjxzcde5ZN3NfOrlw4ST+bSU+t4f4X9nWEOdUcIx+w97GhymUD+FuAGHuLE2kTbShPayGzOwJjhDS1bnc9qokJLS2c+zqKaIDesaeSchhpymZ9882gf9zS18PvXjg6uX59b6ef6NQ1cvmJB3juTfR6nDlLFDN2vUKxNZ79LfzlwwsCcwYj9DErFkoExU0uuu5kHtLSHuLe5hd+8cphU+pVnVrmPj69u4IPnLjhhojoXHpeLqqCHysDM2q9QrGTwj8McVlX9RiHB5cOSgTFTUyTu7FHIdchmf1eYjc0tPLHrMIl0VqgJernmwno+fMHCvJeViggVfg/VQe+MqINUrGTwtxnfBoAPAq+o6s2Fh5gbSwbGTG2hWIL2vljO8wCHeyLct7k1PYfgvHZVBjx8dOUiPnLBIioD2TfaGarM5ySFoG/6TjaXZJ+BiPiBR1T1fYUElw9LBsZMDz2RON2heM5Job0vyk+2tPGLHQeIFNh9bajpPK9QqmRQCzSr6qmFBJcPSwbGTC990QRdoRN3M2ejKxTjga1O97VQ7MTuax9fVc+sCn/eMWX2bZ4u+xWKNUz0Escnj93AHJzm9v9ZlChzYMnAmOkpczdzLnrCcR5+YT8/zei+5nULHzhnAdetbsi5+1qm6dS3uVjJYHHGtwngsKrmtjSgSCwZGDN9qSrd4ThdoTipHEcu+qMJfj5M97X3nT2f69fk3n1tqIDX2cRW7p+adZAmtDaRiLwF9OLsWk6MGIjIaqAJuFZVHxztMS0ZGDP9JVNOUugJ554UwvEkj6a7r7VndF+77Mx53LimkcZZuXVfG2qgDlKlf2r1bZ4MyWCVqh4b5T5u4EkgAvyXJQNjprdcSm0XkhRiiRSP7TzIxuYTu6+947Q53LiukeVzKgr6d7hEqAw4q5CmwrzCVEgGfw3EgdXAo5YMjJm+8i21nUim6AzF6Y3Ec37OeDLFky8f5p5NJ3Zfu3j5LG66aHFe3dcyTZV5hdGSwXikMgWeEJGtInLL0BtFZBHwEeC2cYjFGDPBfvDMHrxuocznLN0s83nwuoUfPLNn1PM8bhdzKv3U15blvMnM63ZxxTkL+O+b1/D3l59BY50zTPSHN9u5dcM2vvzQS+zc3533v0lV6YsmONAVpq0zRE8knlP5jclgPGZBLlbVAyIyF3hSRF5V1Wcybv934EuqmhxtTW86kdwC0NjYWMp4jTElVEi5bHD2AcyvDhCJJ2nvjxEdow1nJrdLeM9Z83jXGXN59vVjbNi0jz1H+2ne20Hz3g4uaKxh/dpGzm+oyXuPQSxxvEVnZcBLZcCDdyoMIY1n9hKRrwN9qvqdjGN7cYbxAGYDIeAWVf3ZSI9jw0TGTF2FNNIZTr7LUQFSqjz/ZjsbmlrYfbh38PjZC6u4ad1iVi/Jr/vaUOV+D1WBid/dPGHDRCJSLiKVA1/jtMzcmXkfVV2qqktUdQnwIPBnoyUCY8zUVqxy2QMq/B7qa4PMqvDnXHTOJcLFp8zmezdewLeuPoezF1YBsOtAD19+6CU+f882nnv9WM4T10P1RxMc7A7T2jF5h5BKemUgIsuAh9PfeoB7VfVfRORWAFW9bcj9f4RNIBsz7RWrXPZQqfTKo+48Vh6BM/a/vbWLDZtaeKGla/D4stnlrF/XyJ+cOqcoVU4nanez9UA2xswoA93OeiKJvN+FZ3ZfG9CY7r52WY7d10Yy3quQLBkYY2akeDJFZ3/ufRQyvXa4l7ub9vGHN443dlxQHeCGNY289+x5RZsc9nmOb2QrVYE8SwbGmBktn5VHQ+052sc9m1p4eveJ3deuW93AFefk331tKLfL6SddFSx+4x1LBsYYQ2Erjwa0dIS4d9OJ3dfqyn1cu6qeD563MO/ua0OJCOV+pxaS31O0x7RkYIwx4EwS90ScktnJVP6vf/u7wtzX3Mrjuw4Ndl+rDnr52IX1XHX+wqIWswt4nXmFcp+7oCEkSwbGGDNEKqV0pVceFfI6eCTdfe2XGd3XKvwerl65iKsvWERVMP9GO0N5XC4qAx4q81yFZMnAGGNGkEim6AjF6IsUVpF/uO5rZT43V52/kGsurKe2zFeMcAHSZTzcOW9ks2RgjDFjKMYkM0B3KM6D29p4+IX9g93X/B4XV563gGtXNRTUfW04uZTTtmRgjJl2cimDnYveSJzO/jiJ1ImTzM17OrhvcysHe8IsqApy3eoG1iyrG/VxfvbCAR7c1kZv+qpDgIDXxdJZFXziosWjnp8rlwgV6XLaQ5e7DvysfvoP18diR/YOm40mf/UkY4wZYqAM9pHeCDVBL0d6I3ztkV08/eqRgh+7MuClvjZ4wlh/854OvvvU67T3R6kKeGjvj/Ldp16neU/HqI9z00WL2fina7n87Pm4xCnhHI6nePlQD//06Ms89tKhguMdkFKlJxyntSPE4Z4IkfQVTubPCk2NOBZmycAYM+XkWwY7Wy6XMLvCz6LaIH6vm/s2t+JxOf0XBOezxyXct7l1zMcq83k42B1hQXWAORW+wb0D4XiSf31iN9987FVa2rOr2Jqt/nQ57QNdYb739JuDP6vRTM1GnsaYGa3QMtjZ8nvcLKoJcqQvQvmQF9OA18WhnnBWj3OwJ0xVwEO5zxnG6Qkn6AjFSKSUJ18+zG9ePly07muZIvEk+zr6qQ54x1xGa8nAGDPlNNSWnVQGOxxPUl9bWG/jkSyuK+dwTxi/x01SFRQi8RTzq4JZnb+gKkh7f5Sg141LhJoyLz6PICIkVTnQFeHp147y9GtHuXj5LNavW8zp8wvrvnbSc4+xPcGGiYwxU06xy2Bn83yJFMSSKbwuIZpMkkgp161uyOr861Y3kEgp4XgSxfmcVPjzS0/hx59ew1euOIPFGd3XPn/PNr780xcL6r429LkjY6ySstVExpgpqVRlsLN9vpsvXsI59TWEYtntTxhYjXSoJ8z8YVYjpVSd7mtN+3jzaP/g8fMbali/rpELCui+1ryng/u3tPLLf1o/4moiSwbGGFOASDxJR39szHfe2VJVnt/Tzt1NLew+dGL3tfXrGlmzpC6vpOB2CUtmV9g+A2OMKaVQLEF7X2FF8DKpKlv2dbKhaR8v7e8ZPH7avArWr13M206ZhSuHpGDJwBhjxomq0hWK01VgvaOhdrR2saFpH1uHdF+7cW0jl5yWXfc1SwbGGDPO4skUHf0x+gtoqjOcXQe62dDUwqa9xze7NdQGuXFtI5edOW/UpGDJwBhjJkg4luRYX7RoQ0cDXjvcy4amFp5749jgsQXVAa5f08j7Rui+ZsnAGGMm0ED/hO7QyfWOCrX3WD8bmvad1H3t2tUNXLFiPv6MRjuWDIwxZhJQVbrT/RMKaaoznJaOEBubW3jy5RO7r318VT1XpruvTXgyEJG3gF4gCSSGBiIiNwJfSn/bB3xeVXeM9piWDIwxU9VAU52ecJxUkV9/D3aH2djcyq93Hu++VhXw8LFV9Vy9sp4Vi2omPBmsUtVjI9z+NuAVVe0UkcuBr6vq2tEe05KBMWaqS6aUrlCMnkiiqCuPAI72Rrl/cyuPvnSQWLrRToXfw65vvH/EZDDh5ShU9Y+q2pn+tgmon8h4jDFmPLhdwqwKPw21QSoD3oJ6Gw81p9LPX7zrFO797Fo+vqqegNdF3xgrm8YjGSjwhIhsFZFbxrjvZ4DHhrtBRG4RkS0isuXo0aNFD9IYYyaCx+1iTqWfRTVBKvzFrR1aV+7j1ncsZ+Nn13HTusZR7zsew0QLVfWAiMwFngS+oKrPDHO/dwLfA96uqu2jPaYNExljpqtowilvEY4Vp7zFgLEmkEt+ZaCqB9KfjwAPA2uG3kdEzgXuAK4aKxEYY8x05ve4WVAdZEF1cNj9AqVS0mcSkXIRqRz4GngvsHPIfRqBh4CbVPW1UsZjjDFTRdDnpr42yKxyf041iPJV6uY284CH0xMjHuBeVf21iNwKoKq3AV8DZgHfS9/vpOWnxhgzE4kI1WVeKgIeOvpj9EbiJXuukiYDVd0DnDfM8dsyvv4s8NlSxmGMMVOZ2yXMqfRTFfTQ3le8ctmZJnxpqTHGmOz4PW4W1gSZWxUo+nyC9UA2xpgppsLvodznpiecoCscK0p5C0sGxhgzBWXOJxRjJ7MNExljzBQ2sJN5UU2QMl/+7+8tGRhjzDTg87iYXx1gfnV+8wmWDIwxZhop83morw1SV+7Lqd6RJQNjjJlmRISaMh8NtdnXO7IJZGOMmaY8bhdzqwJUxZN0h0ffsGbJwBhjprmA100gowXmcGyYyBhjjCUDY4wxlgyMMcZgycAYYwyWDIwxxmDJwBhjDJYMjDHGYMnAGGMMlgyMMcYAUkj964kiIkeBfRMdRxZmA8cmOogcTLV4YerFbPGWlsU7usWqOme4G6ZkMpgqRGSLqq6a6DiyNdXihakXs8VbWhZv/myYyBhjjCUDY4wxlgxK7faJDiBHUy1emHoxW7ylZfHmyeYMjDHG2JWBMcYYSwbGGGOwZFAQEfkbEdklIjtFZKOIBESkTkSeFJHX059rM+7/9yLyhojsFpH3TVDMf5WOd5eI/HX62KSJWUT+S0SOiMjOjGM5xyciF4rIS+nb/kNy6QxeeLwfS/98UyKyasj9J2O8/yoir4rIiyLysIjUTPJ4/zkd63YReUJEFk7meDNu+x8ioiIye7LEewJVtY88PoBFwF4gmP7+J8CngP8DfDl97MvAt9NfnwXsAPzAUuBNwD3OMa8AdgJlOC1PfwOcOpliBi4BVgI7M47lHB/QDFwECPAYcPk4xnsmcDrwNLAq4/hkjfe9gCf99benwM+3KuPrvwRum8zxpo83AI/jbJadPVnizfywK4PCeICgiHhwXmAPAFcBP07f/mPgw+mvrwLuU9Woqu4F3gDWjG+4nAk0qWpIVRPA74GPMIliVtVngI4hh3OKT0QW4LxgPK/OX9Z/Z5xT8nhV9RVV3T3M3SdrvE+k/z8ANAH1kzzenoxvy4GBVTCTMt60fwP+Z0askyLeTJYM8qSq+4HvAC3AQaBbVZ8A5qnqwfR9DgJz06csAlozHqItfWw87QQuEZFZIlIGXIHzjmUyxwy5x7co/fXQ4xNtKsR7M847UZjE8YrIv4hIK3Aj8LX04UkZr4h8CNivqjuG3DSp4rVkkKf0uPVVOJd3C4FyEVk/2inDHBvXdb2q+grOMMCTwK9xLlETo5wy4TGPYaT4JmvckzpeEfkqzv+HewYODXO3SRGvqn5VVRtwYv2L9OFJF2/6TddXOZ6wTrh5mGMTFq8lg/y9G9irqkdVNQ48BLwNOJy+zCP9+Uj6/m0478IH1OMMK40rVb1TVVeq6iU4l7OvM8ljJvf42jg+1JF5fKJN2nhF5JPAB4Eb00MTMInjzXAv8NH015Mx3uU4bxh3iMhb6efeJiLzmWTxWjLIXwuwTkTK0jP9lwGvAI8An0zf55PAz9NfPwJcJyJ+EVmKM3HbPM4xIyJz058bgauBjUzymHONLz2U1Csi69K/m09knDORJmW8IvJ+4EvAh1Q1NAXiPTXj2w8Br07WeFX1JVWdq6pLVHUJzgv9SlU9NOniLfUM9XT+AP4J5z/iTuBunFUBs4Df4rzj/i1Ql3H/r+KsGNjNOKwOGCHmZ4GXcYaILksfmzQx4ySng0Ac5w/nM/nEB6xK/17eBP6T9G77cYr3I+mvo8Bh4PFJHu8bOGPX29Mft03yeH+afu4XgV8AiyZzvENuf4v0aqLJEG/mh5WjMMYYY8NExhhjLBkYY4zBkoExxhgsGRhjjMGSgTHGGCwZGGOMwZKBmQFE5I853v9SEXm0VPGM8rwLReTB8X5eY8CpumnMtKaqb5voGMYiIh5VPQBcM9GxmJnJrgzMtCcifenPl4rI0yLyYLqZyz0DTUNE5P3pY8/hlOkYOLc83bBks4i8ICJXpY//h4h8Lf31+0TkGREZ9u9JRH4kIreJyLMi8pqIfDB9/FMi8oCI/AJ4QkSWDDRFERG3iHwn3eDkRRH5Qvr4hSLyexHZKiKPD9RsMqZQdmVgZpoLgLNxCn/9AbhYRLYAPwTehVOa4f6M+38VeEpVbxanA1iziPwGp8nOZhF5FvgP4ApVTY3yvEuAd+AULvudiJySPn4RcK6qdojIkoz734JT4OwCVU2I0+3NC/y/wFWqelRErgX+BafstDEFsWRgZppmVW0DEJHtOC/SfTgVaF9PH9+A82IMThewD4nI/0h/HwAaVfUVEflT4Bngb1T1zTGe9yfpZPG6iOwBzkgff1JVh2uG8m6cGkEJgHSyWIHTre7J9AWNG6cOjjEFs2RgZppoxtdJjv8NjFSkS4CP6vCdy84B2nH6WYxl6OMPfN8/yvMOPUeAXap6URbPZ0xObM7AGKfy7FIRWZ7+/vqM2x4HvpAxt3BB+vNi4G9xhp0uF5G1YzzHx0TElX6OZThVKkfzBHCrOC1VEZG69DlzROSi9DGviJyd7T/SmNFYMjAznqpGcIaFfpmeQN6XcfM/A17gxfTk7j+nE8OdwP9IrwD6DHCHiARGeZrdOD2nHwNuTT/naO7A6ZnxoojsAG5Q1RjOaqNvp49tx2moZEzBrIS1MSUmIj8CHlVV20NgJi27MjDGGGMTyMYUizgN5T825PADqvqpCQjHmJzYMJExxhgbJjLGGGPJwBhjDJYMjDHGYMnAGGMM8P8D5e+FnezV2scAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df_index['index_price'],df_index['unemployment_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.90115511,  0.37908503],\n",
       "       [ 1.31077107, -1.48187786],\n",
       "       [-0.90115511,  1.30956648],\n",
       "       [ 1.31077107, -0.55139641],\n",
       "       [ 1.31077107, -1.48187786],\n",
       "       [-0.16384638,  0.68924552],\n",
       "       [-0.90115511,  0.999406  ],\n",
       "       [ 1.31077107, -1.48187786],\n",
       "       [ 1.31077107, -1.17171738],\n",
       "       [-0.90115511,  1.30956648],\n",
       "       [-0.90115511,  0.999406  ],\n",
       "       [-0.90115511,  0.37908503],\n",
       "       [-0.90115511,  0.999406  ],\n",
       "       [ 0.57346234, -0.8615569 ],\n",
       "       [-0.16384638, -0.24123593],\n",
       "       [-0.90115511,  0.06892455],\n",
       "       [-0.90115511,  0.999406  ],\n",
       "       [ 1.31077107, -0.8615569 ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regression=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "## cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "validation_score=cross_val_score(regression,X_train,y_train,scoring='neg_mean_squared_error',\n",
    "                                cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5914.828180162396"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(validation_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "## prediction\n",
    "y_pred=regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1180.7466813 ,  802.74279699, 1379.83457045,  838.52599602,\n",
       "        973.85313963, 1144.96348227])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8108.567426306611\n",
      "73.80444932337099\n",
      "90.04758423359624\n"
     ]
    }
   ],
   "source": [
    "## Performance Metrics\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "mse=mean_squared_error(y_test,y_pred)\n",
    "mae=mean_absolute_error(y_test,y_pred)\n",
    "rmse=np.sqrt(mse)\n",
    "print(mse)\n",
    "print(mae)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7591371539010254\n",
      "0.5985619231683756\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "score=r2_score(y_test,y_pred)\n",
    "print(score)\n",
    "#display adjusted R-squared\n",
    "print(1 - (1-score)*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2768e1cd3d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8     -21.746681\n",
      "16    168.257203\n",
      "0      84.165430\n",
      "18     45.474004\n",
      "11    101.146860\n",
      "9      22.036518\n",
      "Name: index_price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "residuals=y_test-y_pred\n",
    "print(residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2768dee0520>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J194QQUJ/C5TPNLX3cLS6lSXa+2FYoSHy4ai4Pl2yPuBpACuf2V3uXoJ9yRTt/zuSFfnpnlFxzXaXoiymAax8ZtepRiLDQpiblWB3KX7tmukphIeK9oYIAhrAymd2nWpgwaREIsOCZ/n5SxEfFc6S3GQdlhwENICVT5zv6uXI2fMsztXmB2/cMDONk3XtlNfrqLhApgGsfGJPeRMuA0v1AZxXbsx3rxW39aj2hghkGsDKJ3adaiQ8VFgwabzdpTjCpOQY8nRUXMDTAFY+setUA3OzEomO0PZfb63IT9dRcQFOA1hZrqOnj0NVLdr/9yLdmJ9Gn8vwzjEdFReoNICV5fZWNNPnMjr95EVaMGk8SbER2gwRwDSAleV2nWogRKAwRwP4YoSGCNfNSOWtY3U6Ki5AaQAry+0qa2R2ZgJxkTr/78VaMTOdlk4dFReoNICVpbp6+9l/ulnbfy+RjooLbBrAylL7TzfT0+9iiQ7AuCQ6Ki6waQArS+0qa0QEFmn77yVbka+j4gKVBrCyVFFFIzPS40mICbe7FMe6MCpO74IDjwawsky/y7CvspkrJuvot8uRnRTD9PQ4naQ9AGkAK8scq26lrbuPwhwN4Mu1Ij+d3eWNtHTqqLhAogGsLLOnwj0Be+Fkbf+9XCtmpulacQFIA1hZZk9FE6nxkWSNj7a7FMfTUXGBSQNYWaaooonCyeMREbtLcTwdFReYNICVJWrOd1HV1KkP4MbQjfnuUXF7KprsLkWNEQ1gZYkLIaEBPHauzvOMitNJ2gOGBrCyxJ6KJiLDQpg1URfgHCvxUeEsnaKj4gKJBrCyRFFFE/OyEokI079iY2nFzDTK6to5paPiAoL+dqgx19nTT/GZFq7Q/r9jbsWFteL0LjggaACrMXewyj0B+xW6/tuY01FxgUUDWI25Is8DuIX6AM4SOioucGgAqzG3t6KJKamxJMVG2F1KQPpwrTgdFed4GsBqTLlchj2V7gEYyhrzs3VUXKDQAFZjqqy+neaOXp3/wUKhIcL1M9J4W0fFOZ4GsBpTFybg0fZfa92Yn0ZLZ++H7e3KmTSA1ZgqKm8iMSacqamxdpcS0K6enkp4qPCmjopzNA1gNab2VDZxxSSdgMdqcZFhOiouAFgawCKyUkSOiUipiDw5xOciIk95Pj8oIgtHO1ZEvunZd7+IvCEiE628BuW9xvYeyuradQCGj+ioOOezLIBFJBT4CbAKKAAeEJGCQbutAvI8r7XAT7049nvGmLnGmPnA68DXrLoGdXH2etoj9QGcb+ioOOez8g54MVBqjCkzxvQALwFrBu2zBnjeuO0EEkUkY6RjjTHnBxwfCxgLr0FdhKKKJsJDhblZOgGPL2QnxTAjPV6bIRzMygDOBE4PeF/l2ebNPiMeKyLfEpHTwKcY5g5YRNaKSJGIFNXVaYd1X9hb0cSsiQlEhYfaXUrQWJGfxu7yJlo6dFScE1kZwEM9hRl8tzrcPiMea4z5qjEmG3gB+MJQJzfGPGOMKTTGFKampnpZsrpUPX0uDlTpCsi+tiI/nX6X4e3j2hvCiawM4Coge8D7LOCsl/t4cyzAr4FPXHal6rIdPttCd59LR8D52PzsRJJjI7Q7mkNZGcC7gTwRyRWRCOB+YN2gfdYBD3p6QywFWowx50Y6VkTyBhx/B3DUwmtQXtqrK2DYwr1WnI6KcyrLAtgY04e7eWATUAK8YowpFpFHReRRz27rgTKgFPgZ8NhIx3qO+Y6IHBaRg8DNwN9adQ3Ke0XlTWQnRZM2LsruUoKOjopzrjArv7kxZj3ukB247ekBXxvgcW+P9WzXJgc/Y4x7Ap6rpqXYXUpQunp6KhGhIWwtqWHplGS7y1EXwas7YBH5nYjcKiI6ck59zOnGTupau3X+B5vERYaxZEqSTtLuQN4G6k+BvwBOiMh3RGSmhTUphynyTMCjD+Dsc2N+OmX17ZTVtdldiroIXgWwMWaLMeZTwEKgHNgsIttF5LMiEm5lgcr/7aloIj4yjOnp8XaXErRW5KcB6F2ww3jdpCAiycBDwOeAfcB/4g7kzZZUphxjT0UT8yclEhqiE/DYJWt8DDMn6Kg4p/G2Dfj3wHtADHC7MeYOY8zLxpgngDgrC1T+7XxXL8dqWnX+Bz9wY346RRVNNLX32F2K8pK3d8A/N8YUGGO+7emni4hEAhhjCi2rTvm9fZXNGKP9f/3BzbPco+K26qAMx/A2gP/fENt2jGUhypn2VDQRIjB/UqLdpQS9OZkJZCREsam42u5SlJdG7AcsIhNwT4ITLSIL+PMcDeNwN0eoILenopGZE8YRF2lpl3LlBRHh5oJ0Xi46TWdPP9EROimSvxvtDvgW4N9xz8XwH8D3Pa9/AP7Z2tKUv+vrd7GvsplCnYDdb9wyawJdvS5dst4hRrxtMcY8BzwnIp8wxvzORzUphzha3UpHT7+2//qRRblJJESH88aRalbOnmB3OWoUozVBfNoY8ysgR0T+YfDnxpj/sKwy5ff26AQ8fic8NIQV+WlsLamlt99FeKgOXvVno/10LixtGwfED/FSQWxPRRMTxkWRmRhtdylqgJsLJtDS2cvuU412l6JGMVoTxH97/vyGb8pRTrKnookrJusKyP7m2umpRIWHsKm4mit1giS/5u1AjO+KyDgRCReRrSJSLyKftro45b/OtXRyprlTmx/8UHREKNfkpfLGkRrcEw4qf+VtA9HNnsUwb8O9WsV04B8tq0r5PW3/9W83z5rAuZYuDp1psbsUNQJvA/jChDurgReNMdq4FOSKypuIDg+lYOI4u0tRQ1gxM43QENFBGX7O2wB+TUSOAoXAVhFJBbqsK0v5u72VTczLTtCn7H5qfGwEi3OSeKNYJ+fxZ95OR/kksAwoNMb0Au3AGisLU/6ro6eP4rPntfnBz90yK50TtW06R7Afu5jbl3zgPhF5ELgH93psKggdON1Cv8voDGh+7qZZ7oEYbxzRu2B/5W0viF/iHpJ8FbDI89JZ0ILUHs8KGAt0Ah6/lpkYzZzMBG0H9mPezqBSCBQY7dOicPeAyEuLIzEmwu5S1ChuLkjn+5uPU3O+i3RdsdrveNsEcRjQgeUKl8t8OABD+b9bPPNBbNZmCL/kbQCnAEdEZJOIrLvwsrIw5Z9K69o439WnAewQeWlx5KbEajOEn/K2CeLrVhahnOPCAIzCHH0A5wQX5gj+n/dP0dLZS0K0rqHrT7zthvYO7tWQwz1f7wb2WliX8lNF5U0kx0aQk6zz8TvFzbMm0OcyvH1MlyryN972gvgr4LfAf3s2ZQJ/sKgm5cf2VjaxUCfgcZQF2YmkxUey8bA2Q/gbb9uAHweWA+cBjDEngDSrilL+qb6tm1P17dr+6zAhIcItsybw9rE6Onv67S5HDeBtAHcbYz5c61pEwgDtkhZkPmz/1QB2nFWzJ9DZ2887x7UZwp94G8DviMg/416c8ybgN8Br1pWl/NHeiiYiQkOYnZlgdynqIi3OTWJ8TDgbtBnCr3gbwE8CdcAh4PPAeuD/WFWU8k9FFU3MzhxHVLiutus0YaEh3FSQzpsltXT3aTOEv/C2F4QL90O3x4wx9xhjfqaj4oJLd18/h6patPuZg62anUFrdx/bSuvtLkV5jBjA4vZ1EakHjgLHRKRORL7mm/KUvzh8poWefhcLJ2n7r1NdOS2Z+MgwNhzSZgh/Mdod8N/h7v2wyBiTbIxJApYAy0Xk760uTvkPXQHD+SLDQlmRn8bmkhp6+112l6MYPYAfBB4wxpy6sMEYUwZ82vOZChJF5U1MTo4hNT7S7lLUZVg5O4Pmjl52lemiNv5gtAAON8Z8rMHIGFPHn5cpUgHOGJ2AJ1BcOz2V6PBQNhw+Z3cpitEDuOcSP1MBpKKhg4b2Hg3gABAdEcr1M1PZVFxDv0ufo9tttACeJyLnh3i1AnN8UaCy3+5y9z9XF2kPiICwcnYG9W3dH7brK/uMOBuaMUY7fCqKyptIiA5nWmqc3aWoMXDDzDQiwkLYcPgci3P1f6p20iVt1ah2VzRSOHk8ISE6AU8giIsM45q8FDYdrka789tLA1iNqKGtm7K6dh2AEWBWzs7gbEsXB6ta7C4lqGkAqxFdaCdclKMP4ALJTfnphIWIzg1hMw1gNaIinYAnICXEhLNsajIbD5/TZggbaQCrEe0ub2RuVoJOwBOAVs3OoLyhg6PVrXaXErQ0gNWwunr7OXxGJ+AJVDfPSidE0GYIG2kAq2EdON1Mb7/R9t8AlRIXyaKcJDbqqDjbaACrYRXpBDwBb9XsCRyvaeNkXZvdpQQlDWA1rN3ljeSlxZEYE2F3KcoiK2dnAOiCnTbRAFZDcrncE/Bo+29gm5AQxYJJiTo5j000gNWQjte20trVp+2/QWDlrAkcPnOe040ddpcSdCwNYBFZKSLHRKRURJ4c4nMRkac8nx8UkYWjHSsi3xORo579XxWRRCuvIVjtLr8wAEPvgAPdKm2GsI1lASwiocBPgFVAAfCAiBQM2m0VkOd5rQV+6sWxm4HZxpi5wHHgK1ZdQzArKm8kfVwkWeOj7S5FWWxScgwFGeO0GcIGVt4BLwZKjTFlxpge4CVgzaB91gDPG7edQKKIZIx0rDHmDWNMn+f4nUCWhdcQtIrKmyicnISITsATDFbNnsDeymaqW7rsLiWoWBnAmcDpAe+rPNu82cebYwEeBjYMdXIRWSsiRSJSVFdXd5GlB7ezzZ2cae6kUNt/g8aqORMA2FSszRC+ZGUAD3XrNHjQ+XD7jHqsiHwV6ANeGOrkxphnjDGFxpjC1NRUL8pVFxRVaPtvsJmWFs+0tDjWH9JmCF+yMoCrgOwB77OAs17uM+KxIvIZ4DbgU0ZnEhlzReWNxEaEMnNCvN2lKB9aPSeD3eWN1LZqM4SvWBnAu4E8EckVkQjgfmDdoH3WAQ96ekMsBVqMMedGOlZEVgJfBu4wxmi/GQvsLm9i4eTxhIVqL8VgcuucDFwGNmlvCJ+x7DfM86DsC8AmoAR4xRhTLCKPisijnt3WA2VAKfAz4LGRjvUc82MgHtgsIvtF5GmrriEYne/q5Wj1eR1+HISmp8cxNTWWP2kzhM+MuCbc5TLGrMcdsgO3PT3gawM87u2xnu3TxrhMNcC+ymaM0fbfYCQi3Dongx+/VUpdazep8ZF2lxTw9N+Y6iN2lTUQFiLMz060uxRlg9Vz3c0QG7U3hE9oAKuP2HWqkTlZCcRGWvqPI+WnZqTHMzU1lvUHtRnCFzSA1Yc6e/o5WNXMktxku0tRNrnQDLHrVAP1bd12lxPwNIDVh/ZWNtHbb1gyRdt/g9mHzRDaG8JyGsDqQ7vKGggRKNQeEEFtRno8U1Jj+ZM2Q1hOA1h9aOepRmZnJhAfFW53KcpG2gzhOxrACnAvwLn/dDNLcrX5QblHxWkzhPU0gBUA+08309Pn0gdwCoCZE+KZkhKrc0NYTANYAbCrrBERWKR3wApPM8TcDHaWaTOElTSAFQC7TjWQP2EcCdHa/qvcLjRD6BSV1tEAVvT0udhb2aTdz9RHaDOE9TSAFQermunq1fZf9VEiwuo5Gew4qc0QVtEAVuw61QjAYm3/VYNoM4S1NIAVO8samJEeT1JshN2lKD+TnxFPrjZDWEYDOMj19rvYU6Htv2po7maICew42UCDNkOMOQ3gIHfoTAsdPf3a/quGdeuciZ5miBq7Swk4GsBBbsfJBgC9A1bD0mYI62gAB7ltpfXMnBBPSpyufqCGdqEZYvvJem2GGGMawEGsq7efooomrpqWYncpys/9uTeENkOMJQ3gILanoomePhfLNYDVKAoyxpGbEsvrB8/aXUpA0QAOYu+X1hMWItr/V41KRLjdMzdEbWuX3eUEDA3gILa9tJ4FkxJ1/TflldvnuXtD6HpxY0cDOEi1dPRy8EyLNj8or+WlxzNzQjyvaQCPGQ3gILWjrAFj0ABWF+X2eRPZU9FEVVOH3aUEBA3gILWttJ7YiFDmZyfaXYpykNvnTgTgdb0LHhMawEFq28l6FucmER6qfwWU9yYlxzA/O5HXDmhviLGgv31B6FxLJ2V17dr8oC7J7fMmUnz2PCfr2uwuxfE0gIPQtlL38GMNYHUpbpubgQh6FzwGNICD0LbSelLiIpiRHm93KcqB0sdFsSQ3idcOnMUYY3c5jqYBHGSMMWwrrWfZ1BRCQsTucpRD3T5vIifr2jly7rzdpTiaBnCQKa1to7a1m6um6fST6tKtmp1BWIjw2gHtDXE5NICDzLsn6gFt/1WXJyk2gqvyUrQZ4jJpAAeZt4/VMi0tjqzxMXaXohzu9rkTOdPcyd7KZrtLcSwN4CDS2dPPrlONXDs91e5SVAC4eVY6EWEh2hviMmgAB5GdZQ309Lm4boYGsLp88VHh3DAjjdcPnqOv32V3OY6kARxE3jleR3R4KItydPpJNTbuXDCR+rZutnmWtlIXRwM4iLx9rJZlU5OJCg+1uxQVIK6fmUZCdDiv7q2yuxRH0gAOEuX17ZQ3dGj7rxpTkWGh3Do3g03FNbR399ldjuNoAAeJd47XAWj7rxpzdy3IpLO3n42Hq+0uxXE0gIPEO8fryEmOYXJyrN2lqABTOHk82UnR/GH/GbtLcRwN4CDQ1dvP9pP1XDcjze5SVAASEe6an8m20npqzut6cRdDAzgIbCutp6vXxYp8DWBljTsXZOIy8Ee9C74oGsBBYEtJDXGRYSzJ1fkflDWmpMYxLzuR3+/VAL4YGsABzuUybC2p5drpqUSE6Y9bWefuBZkcrW6lRGdI85r+Rga4Q2daqG3t5sYCbX5Q1rptrnuGtD/s07tgb2kAB7gtJTWECFw3XQNYWSs5LpJrp6fy6r4zOjTZSxrAAW5LSS2FOUmMj42wuxQVBO4tzKK2tZt3T9TZXYojaAAHsKqmDkrOnedG7f2gfOSGmekkx0bwym4dmuwNDeAAtrWkFoAb89NtrkQFi4iwEO5akMmWkhrq27rtLsfvWRrAIrJSRI6JSKmIPDnE5yIiT3k+PygiC0c7VkTuFZFiEXGJSKGV9TvdpuJqpqbGMiU1zu5SVBD55KJs+lxGH8Z5wbIAFpFQ4CfAKqAAeEBECgbttgrI87zWAj/14tjDwN3Au1bVHgga2rrZWdbA6jkZdpeigsz09HjmZyfy8u7TulzRKKy8A14MlBpjyowxPcBLwJpB+6wBnjduO4FEEckY6VhjTIkx5piFdQeEN47U4DLuxROV8rX7FmVzoraN/aeb7S7Fr1kZwJnA6QHvqzzbvNnHm2PVCNYfOkdOcgz5GfF2l6KC0G1zM4gOD+WVotOj7xzErAxgGWLb4H+PDLePN8eOfHKRtSJSJCJFdXXB1SWmqb2H7ScbWDUnA5Gh/lMqZa34qHBWz8ngtQPn6OjReYKHY2UAVwHZA95nAYNX7xtuH2+OHZEx5hljTKExpjA1NbjmwN1cUkO/y7Bamx+UjT5ZmEVbdx8bDuk8wcOxMoB3A3kikisiEcD9wLpB+6wDHvT0hlgKtBhjznl5rBrGhkPnyBofzezMcXaXooLY4twkcpJjeHm3NkMMx7IANsb0AV8ANgElwCvGmGIReVREHvXsth4oA0qBnwGPjXQsgIjcJSJVwDLgTyKyyaprcKKWzl7eL61ntTY/KJuJCA8snsQH5Y0crdYJeoYiwdBNpLCw0BQVFdldhk+8UnSaf/rtQf7w+HLmZyfaXY4Kco3tPSz99lbuvSKLb901x+5y7DTk3ZCOhAswf9h3hpzkGOZlJdhdilIkxUZw+9yJvLrvDK1dvXaX43c0gANIdUsXO8oauHNBpjY/KL/x4LLJdPT062TtQ9AADiDrDpzBGLhzvnaZVv5jXnYi87IS+OXOCh0ZN4gGcAB5dd9Z5mcnkpOiKx8r//LppZMprW1jR1mD3aX4FQ3gAHHMsxTMXQv07lf5n9vnTSQxJpxf7qiwuxS/ogEcIP6w/wyhIcKtc3XwhfI/UeGh3FeYzRtHajjX0ml3OX5DAzgA9Hum/rs6L4WUuEi7y1FqSJ9aMhmXMby4q9LuUvyGBnAAePd4HedaurivMHv0nZWyyaTkGK6fkcYLuyrp6u23uxy/oAEcAF78oJKUuAhW6MoXys997upcGtp7eFUnawc0gB2v9nwXW4/W8okrsogI0x+n8m/LpiQzO3McP3uvDJdLu6Tpb6zD/WZPFf0uw/2LJtldilKjEhHWXjOVsrp23jxaa3c5ttMAdjCXy/Dy7tMsnZJErvb9VQ6xevYEMhOjeea9MrtLsZ0GsIPtKGugsrFD736Vo4SFhvDwVbl8cKqRfZVNdpdjKw1gB3tuezmJMeGsnD3B7lKUuij3LcomITqcn7x10u5SbKUB7FCVDR1sLqnhU0smERUeanc5Sl2UuMgwHrkqly0lNRSfbbG7HNtoADvU/24/RagIDy7LsbsUpS7JZ67MIT4yjB+/WWp3KbbRAHag1q5eflNUxW1zM0gfF2V3OUpdkoTocD67PIcNh6s5Vt1qdzm20AB2oJd3n6atu49HrppidylKXZaHr8olNiKUH715wu5SbKEB7DD9LsMvtpezKGc8c3TVC+VwiTERfObKHP506FxQrhunAewwrx88S1VTp979qoCx9popxEeG8d2Nx+wuxec0gB2k32V4ausJZqTHc3OBzvugAkNiTAR/fd003jxay64gm7BdA9hB1h86x8m6dp5YMY2QEF3zTQWOh67MIX1cJN/ZeDSoli3SAHYIl8vwozdPkJcWx+rZOum6CizREaH8/Y3T2VfZzKbiGrvL8RkNYIfYWFzN8Zo2vnCD3v2qwHTPFVlMTY3lOxtK6O4LjvmCNYAdoK/fxQ82H2dKaiy3zZ1odzlKWSIsNIT/e/ssyhs6+Pl7p+wuxyc0gB3gpd2nOVHbxj/dMoNQvftVAeya6ancMiudH79ZytnmwF87TgPYz7V29fKDzcdZnJPELbN00h0V+P7ltgJcxvCt9SV2l2I5DWA/919vn6ShvYf/c1s+Inr3qwJf1vgYHr9+Gn86eI53j9fZXY6lNID9WFVTB//z/inuXpDJ3KxEu8tRymfWXjOFqamxfOX3h2jt6rW7HMtoAPspYwxf+2MxoSJ86ZYZdpejlE9FhYfyvXvnca6lk29vOGp3OZbRAPZT6w6c5c2jtXzx5ulMTIy2uxylfG7hpPF87uop/HpXJe+fqLe7HEtoAPuhxvYevvHaEeZlJ/LZ5bl2l6OUbf7hpulMSY3ly787SHNHj93ljDkNYD/0zdePcL6zl3/7xBztdqaCWlR4KD/45HxqW7v40m8OBtwwZQ1gP/PagbO8uu8Mj103lZkTxtldjlK2m5edyFdW5bOlpIb/eT+wBmhoAPuRU/XtfOX3h7hi8nieWJFndzlK+Y3PLs/hllnpfGfDUfZUNNpdzpjRAPYTXb39PP7CXsJChaceWEB4qP5olLpARPjuPfOYmBjN53+5h9ONHXaXNCb0t9wPGGP4+rpijpw7z/fvnUem9npQ6mMSosN59qFCuvtcPPyL3bR0Or9/sAawH/ivt0/y0u7TPH79VFbk60TrSg1nWlo8//3pKzhV387jL+ylp89ld0mXRQPYZn/Yd4bvbTrGmvkT+eJNOuBCqdFcOS2Fb989h/dL63niRWeHsAawjd46Vss//vYAS3KT+O49c3WeX6W8dG9hNl+/vYBNxTU88eJeevudGcIawDbZeLiatc8XkZcWzzN/WUhkWKjdJSnlKA8tz/0whB97YS+dPc6bxF0D2AZ/3H+Gx3+9l1kTE3jxr5aSEBNud0lKOdJDy3P55ppZbCmp4f6f7aS2tcvuki6KBrAPuVyGH245zt++tJ8rJo3nV59bouGr1GX6y2U5PPOXhRyvbuWun2znyNnzdpfkNQ1gH2nr7uPRX+3hh1tOcPfCTJ5/ZDFxkWF2l6VUQLipIJ1XPr+MPpeLO/9rG7/YdsoRw5Y1gH1g+8l6Vv7wXbYereVrtxXw/XvnERWubb5KjaU5WQms/5uruWpaCl9/7QiPPFfk98saiRP+L3G5CgsLTVFRkc/P29LRy/feOMqvdlaSmxLL9+6ZS2FOks/rUCqYGGN4bns5395wlBAR/mZFHo9clUtEmK33m0N2cdIAtkBXbz/PbS/nJ2+V0trdxyPLc/nizTOIjtC7XqV85XRjB9947QhbSmqYnBzDEzfkcef8iYTZM8xfA9hqje09vPhBJc9tL6e2tZvrZqTy5ZUzyc/QWc2UsstbR2v59zeOUXz2PJOTY/jslTnctTCLhGifPgDXALZCb7+L90vrWbf/LOsPnaO7z8XVeSn89bVTuXJaiiXnVEpdHGMMW0pq+fFbpRw43UxUeAi3zZ3IbXMzuHJqii+aJzSAx4IxhurzXew42cA7x+t493gdTR29jIsK49a5E3noyhxmTIgfk3MppcbeoaoWfv1BBa8dOEdbdx/josK4dkYay6cms3xaClnjo61Ygdz3ASwiK4H/BEKBnxtjvjPoc/F8vhroAB4yxuwd6VgRSQJeBnKAcuCTxpimkeq41ADu7OmnsrGDioZ2Ss61cuhMMweqWqhr7QYgJS6Cq/NSWT0ng2ump+hoNqUcpKu3n/dP1LPhcDXvnqj78Pc6KTaCWRPHMTszgYKMceSmxDIpOYZxUZfVZOHbABaRUOA4cBNQBewGHjDGHBmwz2rgCdwBvAT4T2PMkpGOFZHvAo3GmO+IyJPAeGPMl0eq5WID+BuvFbPhUDXV5/88qkYEpqbGMTcrgbmZCRTmJFGQMU7nb1AqABhjKK1tY2dZA4fPnOfw2RaO17TS2//nfEyMCWdSUgz/etccZmcmXOwphgwKK0cCLAZKjTFlACLyErAGODJgnzXA88b9f4GdIpIoIhm4726HO3YNcJ3n+OeAt4ERA/hipY+L4qq8FCYnxTA5JZac5BimpMbpwAmlApSIkJceT176n5sPu/v6OVnbTmVjO5WNHZ5XJ7FjmANWJkomcHrA+yrcd7mj7ZM5yrHpxphzAMaYcyKSNtTJRWQtsNbztk1Ejl3KRfiBFCAw1+R20+tztqC7vl9e2vfZaIxZOXijlQE81C334PaO4fbx5tgRGWOeAZ65mGP8kYgUGWMK7a7DKnp9zqbXd3ms7HtRBWQPeJ8FnPVyn5GOrfE0U+D5s3YMa1ZKKZ+xMoB3A3kikisiEcD9wLpB+6wDHhS3pUCLp3lhpGPXAZ/xfP0Z4I8WXoNSSlnGsiYIY0yfiHwB2IS7K9mzxphiEXnU8/nTwHrcPSBKcXdD++xIx3q+9XeAV0TkEaASuNeqa/ATjm9GGYVen7Pp9V2GoBiIoZRS/kino1RKKZtoACullE00gP2IiNwrIsUi4hKRwkGffUVESkXkmIjcMmD7FSJyyPPZU2LBIHariMhKz/WUekY1Oo6IPCsitSJyeMC2JBHZLCInPH+OH/DZkD9HfyQi2SLyloiUeP5e/q1ne6BcX5SIfCAiBzzX9w3Pdt9dnzFGX37yAvKBGbhH9xUO2F4AHAAigVzgJBDq+ewDYBnuvtMbgFV2X4eX1xrquY4pQITn+grsrusSruMaYCFweMC27wJPer5+Evi30X6O/vgCMoCFnq/jcU8PUBBA1ydAnOfrcGAXsNSX16d3wH7EGFNijBlqxN4a4CVjTLcx5hTuXiOLPf2gxxljdhj335DngTt9V/Fl+XCoujGmB7gw3NxRjDHvAo2DNq/BPUwez593Dtj+sZ+jL+q8FMaYc8YzOZYxphUowT1KNVCuzxhj2jxvwz0vgw+vTwPYGUYasl01xHYnGO6aAsFHhssDF4bLO/aaRSQHWID7LjFgrk9EQkVkP+4BXZuNMT69Pp1dxsdEZAswYYiPvmqMGW5QiWVDtm3k5NovlSOvWUTigN8Bf2eMOT/CYwbHXZ8xph+YLyKJwKsiMnuE3cf8+jSAfcwYc+MlHDbSkO2sIbY7gTdD1Z2qRkQyjHuyqIHD5R13zSISjjt8XzDG/N6zOWCu7wJjTLOIvA2sxIfXp00QzrAOuF9EIkUkF8gDPvD886hVRJZ6ej88iHOGZnszVN2phhsuP+TP0Yb6vOL5O/U/QIkx5j8GfBQo15fqufNFRKKBG4Gj+PL67H4Sqa+PPJW9C/f/ZbuBGmDTgM++ivup6zEG9HQACoHDns9+jGd0oxNeuIehH/fU/lW767nEa3gROAf0en52jwDJwFbghOfPpNF+jv74Aq7C/U/sg8B+z2t1AF3fXGCf5/oOA1/zbPfZ9elQZKWUsok2QSillE00gJVSyiYawEopZRMNYKWUsokGsFJK2UQDWCmlbKIBrBxJRLZf5P7XicjrVtUzwnknishvfX1e5Qw6FFk5kjHmSrtrGI2IhBljzgL32F2L8k96B6wcSUTaPH9eJyJvi8hvReSoiLxwYVJ6z4TvR0XkfeDuAcfGeiZS3y0i+0RkjWf7UyLyNc/Xt4jIuyIy5O+IiPxCRJ4WkfdE5LiI3ObZ/pCI/EZEXgPeEJGcC5O1e2be+nfPBPoHReQJz/YrROQdEdkjIps88w+oIKB3wCoQLABm4Z4YZRuwXESKgJ8BN+Cet/XlAft/FXjTGPOwZy6ADzyz1D0J7BaR94CngNXGGNcI580BrgWmAm+JyDTP9mXAXGNMo2caxwvW4p7Ie4Fxr/yd5Jns5kfAGmNMnYjcB3wLePgS/1soB9EAVoHgA2NMFYBnbtccoA04ZYw54dn+K9wBCHAzcIeIfMnzPgqYZIwpEZG/At4F/t4Yc3KU877iCegTIlIGzPRs32yMGTxJO7gne3naGNMH4Ano2cBsYLPnxj0U99wSKghoAKtA0D3g637+/Pd6uIlOBPiEGXr1kTlAAzDRi/MO/v4X3rePcN7BxwhQbIxZ5sX5VIDRNmAVqI4CuSIy1fP+gQGfbQKeGNBWvMDz52Tgi7ibNFaJyJJRznGviIR4zjEF9wxZI3kDeFREwjznS/IckyoiyzzbwkVklrcXqZxNA1gFJGNMF+4mhz95HsJVDPj4m7jX/zroeUD2zQFz337J03PhEeDnIhI1wmmOAe/gXgz1Uc85R/JzoNJz3gPAXxj3enj3AP/m2bYf8PseHmps6HSUSl0CEfkF8LoxRvv4qkumd8BKKWUTfQin1AhE5KvAvYM2/8YY85AN5agAo00QSillE22CUEopm2gAK6WUTTSAlVLKJhrASillk/8PbyQJhoYFUKMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot this residuals\n",
    "sns.displot(residuals,kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2768e1178e0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## scatter plot with respect to prediction and residuals\n",
    "plt.scatter(y_pred,residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## OLS Linear Regression\n",
    "import statsmodels.api as sm\n",
    "model=sm.OLS(y_train,X_train).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>index_price</td>   <th>  R-squared (uncentered):</th>      <td>   0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>  -0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>  0.2880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 01 Aug 2022</td> <th>  Prob (F-statistic):</th>           <td> 0.754</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:16:49</td>     <th>  Log-Likelihood:    </th>          <td> -150.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    18</td>      <th>  AIC:               </th>          <td>   305.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    16</td>      <th>  BIC:               </th>          <td>   307.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   88.2728</td> <td>  658.295</td> <td>    0.134</td> <td> 0.895</td> <td>-1307.250</td> <td> 1483.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> -116.2572</td> <td>  658.295</td> <td>   -0.177</td> <td> 0.862</td> <td>-1511.780</td> <td> 1279.266</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.598</td> <th>  Durbin-Watson:     </th> <td>   0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.741</td> <th>  Jarque-Bera (JB):  </th> <td>   0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.361</td> <th>  Prob(JB):          </th> <td>   0.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.517</td> <th>  Cond. No.          </th> <td>    4.78</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:            index_price   R-squared (uncentered):                   0.035\n",
       "Model:                            OLS   Adj. R-squared (uncentered):             -0.086\n",
       "Method:                 Least Squares   F-statistic:                             0.2880\n",
       "Date:                Mon, 01 Aug 2022   Prob (F-statistic):                       0.754\n",
       "Time:                        22:16:49   Log-Likelihood:                         -150.85\n",
       "No. Observations:                  18   AIC:                                      305.7\n",
       "Df Residuals:                      16   BIC:                                      307.5\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            88.2728    658.295      0.134      0.895   -1307.250    1483.796\n",
       "x2          -116.2572    658.295     -0.177      0.862   -1511.780    1279.266\n",
       "==============================================================================\n",
       "Omnibus:                        0.598   Durbin-Watson:                   0.007\n",
       "Prob(Omnibus):                  0.741   Jarque-Bera (JB):                0.567\n",
       "Skew:                          -0.361   Prob(JB):                        0.753\n",
       "Kurtosis:                       2.517   Cond. No.                         4.78\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  88.27275507 -116.25716066]\n"
     ]
    }
   ],
   "source": [
    "print(regression.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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